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5Seeing Faces in the Brain
Alumit Ishai
“It is with our faces that we face the world, from the moment of
birth to the moment of death.
Our age and our gender are printed on our faces. Our emotions,
the open and instinctive emotions that Darwin wrote about, as
well as the hidden or repressed ones that Freud wrote about, are dis-
played on our faces along with our thoughts and intentions. Though
we may admire arms and legs, breasts and buttocks, it is the face,
first and last, that is judged “beautiful” in an aesthetic sense, “fine”
or ‘distinguished” in a moral or intellectual sense. And, crucially, it
is by our faces that we can be recognized as individuals. Our faces
bear the stamp of our experiences and our character; at forty, it is
said, a man has the face he deserves.”
Oliver Sacks, “Face-Blind,” The New Yorker, August 30, 2010.
a distributed cortical network for face perception
Face recognition is a highly developed skill in humans. We are able to
identify thousands of faces individually, or to easily pick out familiar
faces in a crowd, distinctions that require a special expertise (Gauthier
and Nelson 2001). The cognitive development of face perception sug-
gests a special status for face processing. Shortly after birth, infants
prefer to look at faces longer than at other objects (Morton and John-
son 1991). The predilection of infants to imitate facial expressions at a
very early age (Meltzoff and Moore, 1977) further suggests that face per-
ception plays a central role in developing social interaction skills and
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118
language. It is therefore not surprising that functional brain imaging
studies have localized a specialized neural system for face perception
in the human brain.
The recognition of facial identity is based on invariant facial features,
whereas animated aspects of the face, such as speech-related movement
and expression, contribute to social communication. When looking at
faces, we rapidly perceive the gender, expression, age, and mood of the
individual. Processing information gleaned from the faces of others
requires the integration of activity across a network of cortical regions.
Converging empirical evidence suggests that face perception is medi-
ated by a distributed neural system (Sergent et al. 1992; Haxby et al.
2000; Ishai et al. 2004, 2005). The cortical network for face perception
includes the inferior occipital gyrus (IOG) and lateral fusiform gyrus
(FG), extrastriate regions that process the identification of individuals
(Kanwisher et al. 1997; Ishai et al. 2000a; Grill-Spector et al. 2004; Rot-
shtein et al. 2005); the superior temporal sulcus (STS), where gaze direc-
tion and speech-related movements are processed (Calder et al. 2007;
Hoffman and Haxby, 2000; Puce et al. 1998); the amygdala and insula,
where facial expressions are processed (Breiter et al. 1996; Morris et al.
1996; Phillips et al. 1997; Vuilleumier et al. 2001; Ishai et al. 2004); the
inferior frontal gyrus (IFG), where semantic aspects are processed (Leve-
roni et al. 2000; Ishai et al. 2000b; 2002), and regions of the reward cir-
cuitry, namely the nucleus accumbens and orbitofrontal cortex (OFC),
where facial beauty and sexual relevance are assessed (Aharon et al.
2001; O’Doherty et al. 2003; Kranz and Ishai, 2006; Ishai, 2007). The
existence of multiple face-selective regions in the human brain is also
corroborated by intracranial recordings in epileptic patients undergo-
ing brain surgery. Face-selective potentials were found in several sites
along ventral occipitotemporal and lateral temporal cortices (Allison et
al. 1999; McCarthy et al. 1999; Puce et al. 1999; Barbeau et al. 2008), as
well as the amygdala and prefrontal structures (Halgren et al. 1994a;,
1994b). It has been suggested that the face network includes “core” extra-
striate regions (IOG, FG, STS) that process the invariant facial features,
and “extended” limbic and prefrontal regions that process changeable
aspects of faces (Haxby et al. 2000).
Seeing Faces in the Brain
119
Interestingly, gender discrimination, an automatic and effort-
less task, seems to be a distributed feature. A recent decoding study
has shown that it is possible to predict with above chance accuracies,
based on the fMRI time series in face-responsive regions, whether
subjects viewed male or female faces. The decoding accuracies were
independent of the subject’s gender (man or woman) or sexual pref-
erence (hetero- or homosexual) (Kaul et al. 2011). Given the evolution-
ary importance of gender discrimination and its fundamental nature
in face processing, it is plausible that there is no “gender-detection
region” in the human brain, but rather gender information is a dis-
tributed attribute that depends on integration of information across
cortical regions.
In many functional Magnetic Resonance Imaging (fMRI) studies of
face perception, a localizer is used to identify the face-selective region
in the fusiform gyrus, the “FFA,” based on stronger responses to faces
than to assorted common objects (Kanwisher et al. 1997). Although
the FFA also responds significantly to other objects (Ishai et al. 1999;
2000a; Haxby et al. 2001), it is commonly believed that the FFA is a
face-selective “module,” namely a cortical region dedicated to the
visual analysis of face stimuli. But is the FFA sufficient or even nec-
essary for face perception? Functional MRI studies in which neural
activity is not manifested by perceptual awareness provide evidence
against sufficiency, whereas studies in which perceptual awareness
is not caused by neural activity provide evidence against necessity.
When activation elicited by face stimuli is compared with activation
evoked by scrambled faces, a distributed neural system of multiple,
bilateral regions is revealed (Figure 1). The activation within visual,
limbic, and prefrontal face-selective regions is stimulus- (e.g. unfa-
miliar, famous, neutral, and emotional faces) and task- (e.g. passive
viewing, attractiveness rating) independent (Ishai et al. 2005; Kranz
and Ishai 2006). These consistent and replicable distributed patterns
of activation are what make faces special “stimuli”: the neural signa-
ture of face perception is not manifested by activation solely in the
“FFA,” but rather by activation within multiple regions that comprise
a network.
Alumit Ishai
120
Figure 1
Top: Viewing faces elicits activation within a distributed cortical network that includes visual, limbic and prefrontal regions. Coronal sections, taken from a representative subject, illustrate activation within the core (IOG, FG, STS) and extended (AMG, IFG, OFC) systems. Coordinates are in the Talaraich space.
Bottom: A model for face perception. Neural coupling among face-selective regions is stimulus-and task-dependent. The model assumes reciprocal connec-tions between all visual, limbic and prefrontal face-selective regions (although the strength of the connections may not be symmetrical.) Viewing emotional faces increases the effective connectivity between the FG and the AMG (yellow), whereas viewing famous, attractive faces increases the coupling between the FG and the OFC (blue). New predictions are shown in dashed arrows: Attention to gaze direction would increase the coupling between the STS and the FG (orange); Viewing animated faces would increase the coupling between the STS and the IFG/OFC (green); Viewing indeterminate, low-spatial frequency faces would result in increased effective connectivity from the OFC to the FG (red).
Seeing Faces in the Brain
121
Consistent with the human studies of face perception, electrophysio-
logical studies in non-human primates revealed face-selective neurons
not only in the temporal cortex (e.g. Bruce et al. 1981; Perrett et al. 1982)
but also in the orbitofrontal (Thorpe et al. 1983) and prefrontal (Wil-
son et al. 1993) cortices. Furthermore, recent fMRI studies in behaving
monkeys found activation in multiple face-selective regions in visual
(Pinsk et al. 2005; Tsao et al. 2006) as well as in limbic and prefrontal cor-
tices (Hadj-Bouziane et al. 2008). The innovative technical development
of fMRI-guided electrophysiology (e.g., Tsao et al. 2006) will enable not
only the identification and functional characterization of all face-re-
sponsive regions in the macaque brain, but also the exploration of the
homology between the face networks in monkey and man (see Tsao et
al. 2008).
face perception and cortical connectivity
With the identification and localization of multiple face-responsive
regions, the effective connectivity within the “face network” can be
quantified. In a pioneer study, conventional statistical parametric map-
ping (SPM) analysis (Friston et al. 1995) was combined with Dynamic
Causal Modeling (DCM, Friston et al. 2003) to investigate the neural cou-
pling and functional organization between and within the core (IOG,
FG, STS) and extended (amygdala, IFG, OFC) systems. It has been found
that during face viewing, the core system is functionally organized in
a hierarchical, feed-forward architecture, with the IOG exerting influ-
ences on both the FG and STS. Moreover, the FG exerted a strong causal
influence on the extended system, namely the amygdala, IFG, and
OFC. Finally, content-specific alterations in functional coupling were
observed within this network: Viewing emotional faces increased the
coupling between the FG and the amygdala, whereas viewing famous
faces increased the coupling between the FG and the OFC cortex (Figure
1). The FG is therefore a major entry node in the cortical network that
mediates face perception (Fairhall and Ishai 2007).
Based on the Fairhall and Ishai (2007) feed-forward model of the
core system, a recent study has shown that the effective connectivity
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between the IOG-FG and the IOG-STS gradually emerges during child-
hood. In children younger than 11 years old the connectivity was sig-
nificantly weaker than the connectivity in adults. Furthermore, in con-
trast with adults, children did not exhibit task-dependent effects on the
strength of the connectivity, suggesting that top-down modulations
develop and increase during childhood (Cohen Kadosh et al. 2011).
Previous DCM studies of face perception have also shown that effec-
tive connectivity between regions is task-specific. For example, viewing
faces was associated with an increase in bottom-up, forward connec-
tivity from extrastriate face-selective regions to the prefrontal cortex,
whereas the generation of mental images of faces was associated with
an increase in top-down, backward connectivity from prefrontal to
extrastriate regions (Mechelli et al. 2004). Similarly, perceptual deci-
sions about faces resulted in an increase in top-down connectivity from
the ventral medial frontal cortex to the fusiform gyrus (Summerfield
et al. 2006).
As we currently do not have sufficient temporal information about
the dynamics of face processing in the human brain, it is perhaps pre-
mature to propose a new functional model for face perception that inte-
grates all available data. When proposing their influential model for
the recognition of familiar faces, Bruce and Young stated, “In under-
standing face processing a crucial problem is to determine what uses
people need to make of the information they derive from faces” (Bruce
and Young 1986, 306). In line with this statement and with the above-
mentioned DCM studies (Mechelli et al. 2004; Summerfield et al. 2006;
Fairhall and Ishai 2007), I have recently suggested a working model for
face perception that accounts for existing findings and from which
new predictions are derived (Ishai 2008). The model depicted in Figure
1 postulates bidirectional connections between all visual, limbic, and
prefrontal face-selective regions (such large-scale integration could be
mediated by synchronization of activity, as suggested by Rodriguez et al.
1999). The model further assumes that the flow of information through
the face network is shaped by cognitive demands, namely that the effec-
tive connectivity between regions depends on the nature of faces and
the task at hand. For example, when we look for a friend in a crowded
Seeing Faces in the Brain
123
place, we have to match incoming visual input with faces stored in long-
term memory, whereas when performing laboratory experiments such
as gender discrimination, we have to focus on or attend to specific facial
features. Consequently, several new testable predictions are suggested:
focusing attention on gaze direction would likely increase the coupling
between the STS and the FG; viewing animated faces would increase
the effective connectivity between the STS and the IFG/OFC; viewing
disgusted faces would increase the coupling between the FG and the
insula. Consistent with a magnetoencephalography (MEG) study, which
showed that the prefrontal cortex generates predictions that influence
object processing in extrastriate regions (Bar et al. 2006), the model also
predicts that indeterminate facial input will increase the top-down
connectivity from the OFC to the FG. Future studies will determine the
extent to which various task demands are indeed associated with differ-
ential coupling among face-selective regions and the temporal dynam-
ics of these activation patterns.
prosopagnosia and activation in the face network
In 1947, the German neurologist Joachim Bodamer described three
patients who were unable to recognize faces but had no other recogni-
tion deficits. Bodamer assumed that this highly selective form of agno-
sia, which he termed “prosopagnosia,” implied that there was a discrete
area in the brain that specialized in face perception. Years later, it was
shown that the inability to recognize familiar faces (Whitely and War-
rington 1977; Damasio et al. 1990; Behrmann et al. 1992) is the result
of bilateral (Damasio et al. 1982) as well as right unilateral lesions in
the ventral occipitotemporal cortex (De Renzi 1986; Landis et al. 1986).
Some prosopagnosic patients, despite their profound inability to recog-
nize faces, exhibit normal patterns of activation in the FFA (e.g. Marotta
et al. 2001; Avidan et al. 2005), suggesting that activation in this region
is not sufficient for face recognition, which likely depends on integra-
tion across cortical regions. Consistently, a recent study has shown that
normal fMRI activation in the ventro-occipital regions of congenital
prosopagnosics is insufficient for intact face recognition, and that the
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124
functional impairment in congenital prosopagnosia is due to disrupted
information propagation between the core and the extended face-pro-
cessing network (Avidan and Behrmann 2009).
The case study of PS, a patient with bilateral and asymmetrical
lesions in right the inferior occipital gyrus (IOG) and left fusiform gyrus
(FG), demonstrates that she is prosopagnosic despite her intact left IOG
and right FG (Rossion et al. 2003; Sorger et al. 2007). PS is therefore a
living proof that bilateral and distributed activation is necessary for
face recognition. Adaptation experiments in this patient have shown
that although her neural response to repeated objects in extrastriate
object-selective regions was reduced, repeated and unrepeated faces
evoked similar activation in the FG (Schiltz et al. 2006). It therefore
seems that while activation in the FFA per se is not sufficient, adapta-
tion in this region may be necessary for face recognition. The FG does
not seem to work in isolation; it is a vital node in a cortical network that
stretches from the occipital to the prefrontal cortex, which mediates
both the recognition of faces that is based on knowledge and the famil-
iarity of faces that is based on feeling and association.
beauty and the brain
Facial beauty is considered a marker for reproductive fitness (Thorn-
hill and Gangestad, 1999). Attributes such as symmetry (Langlois and
Roggman, 1990) and sexually dimorphic features (Perrett et al. 1998)
contribute to the assessment of facial attractiveness. The perception of
beauty has not only biological but also social and economical impact:
good-looking people get promotion and earn more than average-look-
ing people, regardless of occupation, a phenomenon called “the plain-
ness penalty” (Hamermesh and Biddle 1994; Mueller and Mazur 1996;
Senior et al. 2007). With the advent of brain imaging techniques, iden-
tifying the neural correlates of “beauty” is a timely empirical endeavor.
The everyday appraisal of facial beauty seems to be automatic and fast:
event-related brain potentials reveal that attractive faces, as compared
with unattractive ones, elicit an early posterior negativity (around 250
ms) and a late parietal positivity (400–600 ms), suggesting a differential
Seeing Faces in the Brain
125
response within half a second after an encounter with a beautiful face
(Werheid et al. 2007). Functional MRI studies have reported that facial
beauty evokes activation in the extrastriate core system (Chatterjee et
al. 2009), amygdala (Winston et al. 2007), and the reward circuitry (Aha-
ron et al. 2001; O’Doherty et al. 2003), where direct eye gaze (Kampe et al.
2001) and happy expressions (O’Doherty et al. 2003) increase the appeal
of attractive faces. It has been suggested that the rewarding, adaptive
value of an attractive face can be dissociated from its aesthetic value.
An attractive opposite-sex face may signal that a potential sexual part-
ner has a healthy genotype, whereas an attractive, same-sex face cannot
have such reproductive benefits (Senior 2003).
Kranz and Ishai (2006) used fMRI to test whether subjects would
respond more to sexually preferable faces and predicted such modu-
lation in the reward circuitry. Forty hetero- and homosexual men and
women viewed photographs of male and female faces or assessed facial
attractiveness. Behaviorally, regardless of their gender and sexual ori-
entation, all subjects rated the attractiveness of both male and female
faces. Within multiple, bilateral face-selective regions in the visual cor-
tex, limbic system, and prefrontal cortex, similar patterns of activa-
tion were found in all subjects in response to both male and female
faces. A significant interaction between stimulus gender and the sex-
ual preference of the subject was found in the mediodorsal nucleus of
the thalamus and medial OFC, where heterosexual men and homosex-
ual women responded more to female faces, and heterosexual women
and homosexual men responded more to male faces (Kranz and Ishai
2006). Furthermore, a three-way interaction between stimulus gender,
beauty, and sexual preference was found in the OFC, where attractive
male faces elicited stronger activation than attractive female faces in
heterosexual women and homosexual men, and attractive female faces
evoked stronger activation than attractive male faces in heterosexual
men and homosexual women (Ishai 2007). Taken collectively, these find-
ings suggest that the OFC represents the value of salient sexually rele-
vant faces, irrespective of their reproductive fitness. Our data therefore
do not support the proposed neural dissociation between attractive
faces of the opposite-sex that reflect evolutionary benefits and attrac-
Alumit Ishai
126
tive faces of the same-sex that reflect aesthetic appraisal of beauty (Sen-
ior 2003). Rather, our findings demonstrate that the OFC represents the
reward value of faces of potential sexual partners, including same-sex
mates, irrespective of reproduction.
Recent findings suggest that the OFC has a role not only in evaluating
attractive faces, but also in consumer behavior and memory of beauty.
Classical conditioning with attractive faces shows that arbitrary stim-
uli can acquire conditioned value when paired with an attractive face,
as reflected by prediction error-related activity in the ventral striatum
(Bray and O’Doherty 2007). Thus, with repeated exposure to simple asso-
ciations between products and rewarding attractive faces, advertisers
can influence consumer behavior. Furthermore, stronger functional
connectivity between the OFC and the hippocampus was observed dur-
ing encoding of attractive rather than neutral faces, suggesting that sub-
sequent memory of attractive faces depends on the interaction between
the reward circuitry and medial temporal structures associated with
memory formation (Tsukiura Cabeza 2011). Attractive faces therefore
are special “human stimuli” that communicate socio-economical fea-
tures and facilitate memory.
the social brain: self and others
Gallup, who first demonstrated that chimpanzees, and later bonobos
and orangutans, can recognize themselves in the mirror (Gallup 1970;
Suarez and Gallup 1981), postulated that mirror self-recognition leads
to the ability to be introspective, an ability required to infer the men-
tal states of others, known as “theory of mind” (Premack and Woodruff
1978). Human and non-human species that exhibit self-face recognition
have highly developed frontal lobes and live in social groups, where
they constantly need to process information relating to conspecifics.
The human brain is developed and shaped during interaction with
other people, and social interactions stimulate an ability to imagine the
mind of others in order to predict their behavior. Social communication
in all its forms (cooperation, competition, negotiation, etc.) is driven by
the individual’s personality, stereotypes, and attachment style and is
Seeing Faces in the Brain
127
modulated by emotions such as pride, envy, and regret. Faces, which
enable not only verbal but also non-verbal communication, are the ulti-
mate social stimuli and can be used to investigate the neural correlates
of social interactions.
In the past decade, a plethora of studies have localized the neural sub-
strates that mediate the perception of “self” and “others.” What hap-
pens in our brain when we view ourselves? A meta-analysis of nine func-
tional brain imaging studies of self-face recognition revealed that the
left FG, right precuneus, and bilateral IFG are consistently activated,
suggesting that a distributed neural system mediates the processing of
sensory and self-referential information (Platek et al. 2008). If self-aware-
ness and introspection lead to the development of theory of mind, what
happens in our brain when we view others? Perhaps, not surprisingly,
recent studies suggest that we are automatically biased toward famil-
iar faces, same-race faces, and in-group faces. When we view unfamiliar
faces, as compared with personally familiar and famous faces, stronger
activation is observed in the amygdala, which maintains a vigilant atti-
tude toward strangers (Gobbini and Haxby 2007). When we view same-
and other-race faces, a same-race advantage is manifested by differen-
tial neural activation: Faces of the subject’s own race evoke stronger
responses in the FG and are associated with superior recognition mem-
ory, i.e., white faces evoke more activation than black faces in Euro-
pean-Americans, whereas black faces evoke stronger activation than
white faces in African Americans (Golby et al. 2001). When white sub-
jects viewed briefly presented white and black faces, greater response
was observed in the amygdala for black than white faces. Longer expo-
sure to the faces reduced the enhanced response to black faces in the
amygdala and elicited greater activation for black than white faces in
regions of the frontal cortex associated with control and regulation
(Cunningham et al. 2004), suggesting that the automatic concern and
heightened caution evoked by out-group faces can be top-down modu-
lated. Another striking example was reported when participants were
assigned to an arbitrary mixed-race-team, assuming their team would
later compete against another team: greater activity was found in the
amygdala, FG, OFC, and dorsal striatum when participants viewed novel
Alumit Ishai
128
in-group faces than when they viewed novel out-group faces. Moreover,
activity in the OFC mediated the in-group bias in self-reported liking
for the faces. As the in-group bias was independent of the participants’
own race, it is likely that behavioral and neural responses to one’s social
group may occur automatically (Van Bavel et al. 2008).
Mutual trust forms the basis for engagement in cooperation, which
is integral to daily life and a prerequisite for cultural and social evolu-
tion. Assessing an individual’s trustworthiness might be related to a
broader categorization into “good guy/bad guy” (Todorov 2008), guiding
approach versus avoidance behavior (Cosmides and Tooby 2000). Corre-
lations between facial trustworthiness and various other facial judg-
ments, e.g. how caring, happy, or dominant a person is (Todorov et al.
2008), indicate that trustworthiness judgments may summarize
numerous derived trait inferences. A recent meta-analysis of neuroim-
aging studies of trustworthiness judgment found activation in the pos-
terior STS and the amygdala. Moreover, attractiveness judgments over-
lapped with trustworthiness judgments and co-activated the amygdala,
suggesting that socially and evolutionarily important judgments based
on facial aspects may be mediated by a common neural substrate (Bzdok
et al. 2010). Interestingly, a recent study of first impressions reported
that greater activation in the amygdala was correlated with post-scan
higher ratings of faces of Chief Executive Officers’ (CEOs) leadership abil-
ity, a subjective judgment, and greater profits made by the CEOs’ compa-
nies, which is an objective measure (Rule et al. 2011). These new findings
extend the traditional role of the amygdala beyond threat detection,
suggesting that this region also processes positive emotional and social
stimuli that influence mate choice and social exchange.
With the advent of functional brain imaging techniques and the devel-
opment of cutting-edge analytic tools, a shift from “where-” to “how-”
type studies is inevitable. I believe that the descriptive brain imaging
studies that localize activation in the brain in response to a certain face
or during a specific task will be gradually replaced by more mechanis-
tic studies that are embedded in theoretical and computational neuro-
science. These future studies will define the algorithmic nature of the
transformations between seeing a face and the neural response to its
Seeing Faces in the Brain
129
various (visual, emotional, social, and economical) features. One can
only hope that by using face perception as a model for information pro-
cessing we will be able to understand how the human brain works.
acknowledgment
The Swiss National Science Foundation grant 320030-129793 and Swiss
National Center for Competence in Research: Neural Plasticity and
Repair are kindly acknowledged for their support.
Alumit Ishai
130
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